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   "id": "afaf8039",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: __ModuleName__\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3d6f34",
   "metadata": {},
   "source": [
    "# __ModuleName__Embeddings\n",
    "\n",
    "- [ ] TODO: Make sure API reference link is correct\n",
    "\n",
    "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
    "\n",
    "| Provider | Package |\n",
    "|:--------:|:-------:|\n",
    "| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
    "\n",
    "## Setup\n",
    "\n",
    "- [ ] TODO: Update with relevant info.\n",
    "\n",
    "To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
    "\n",
    "### Credentials\n",
    "\n",
    "- TODO: Update with relevant info.\n",
    "\n",
    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36521c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
    "        \"Enter your __ModuleName__ API key: \"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84fb993",
   "metadata": {},
   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39a4953b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9664366",
   "metadata": {},
   "source": [
    "### Installation\n",
    "\n",
    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64853226",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -qU __package_name__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dd1724",
   "metadata": {},
   "source": [
    "## Instantiation\n",
    "\n",
    "Now we can instantiate our model object and generate chat completions:\n",
    "\n",
    "- TODO: Update model instantiation with relevant params."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ea7a09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __module_name__ import __ModuleName__Embeddings\n",
    "\n",
    "embeddings = __ModuleName__Embeddings(\n",
    "    model=\"model-name\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d271b6",
   "metadata": {},
   "source": [
    "## Indexing and Retrieval\n",
    "\n",
    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
    "\n",
    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d817716b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a vector store with a sample text\n",
    "from langchain_core.vectorstores import InMemoryVectorStore\n",
    "\n",
    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
    "\n",
    "vectorstore = InMemoryVectorStore.from_texts(\n",
    "    [text],\n",
    "    embedding=embeddings,\n",
    ")\n",
    "\n",
    "# Use the vectorstore as a retriever\n",
    "retriever = vectorstore.as_retriever()\n",
    "\n",
    "# Retrieve the most similar text\n",
    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
    "\n",
    "# show the retrieved document's content\n",
    "retrieved_documents[0].page_content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02b9855",
   "metadata": {},
   "source": [
    "## Direct Usage\n",
    "\n",
    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
    "\n",
    "You can directly call these methods to get embeddings for your own use cases.\n",
    "\n",
    "### Embed single texts\n",
    "\n",
    "You can embed single texts or documents with `embed_query`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d2befcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_vector = embeddings.embed_query(text)\n",
    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5a7d03",
   "metadata": {},
   "source": [
    "### Embed multiple texts\n",
    "\n",
    "You can embed multiple texts with `embed_documents`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f4d6e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "text2 = (\n",
    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
    ")\n",
    "two_vectors = embeddings.embed_documents([text, text2])\n",
    "for vector in two_vectors:\n",
    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98785c12",
   "metadata": {},
   "source": [
    "## API Reference\n",
    "\n",
    "For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
   ]
  }
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